docstr: Generate a spherical RGB volumetric function and its corresponding mesh
  and check whether MeshesRenderer returns the same images as the
  corresponding ImplicitRenderer.
docstr_tok: ["Generate","a","spherical","RGB","volumetric","function","and","its","corresponding","mesh","and","check","whether","MeshesRenderer","returns","the","same","images","as","the","corresponding","ImplicitRenderer","."]
code: def _compare_with_meshes_renderer(
      self, image_size, batch_size=11, sphere_diameter=0.6
  ):
      """
      Generate a spherical RGB volumetric function and its corresponding mesh
      and check whether MeshesRenderer returns the same images as the
      corresponding ImplicitRenderer.
      """
  
      # generate NDC camera extrinsics and intrinsics
      cameras = init_cameras(
          batch_size, image_size=image_size, ndc=True
      )
  
      # get rand offset of the volume
      sphere_centroid = (
          torch.randn(batch_size, 3, device=cameras.device) * 0.1
      )
      sphere_centroid.requires_grad = True
  
      # init the grid raysampler with the ndc grid
      raysampler = NDCMultinomialRaysampler(
          image_width=image_size[1],
          image_height=image_size[0],
          n_pts_per_ray=256,
          min_depth=0.1,
          max_depth=2.0,
      )
  
      # get the EA raymarcher
      raymarcher = EmissionAbsorptionRaymarcher()
  
      # jitter the camera intrinsics a bit for each render
      cameras_randomized = cameras.clone()
      cameras_randomized.principal_point = (
          torch.randn_like(cameras.principal_point) * 0.3
      )
      cameras_randomized.focal_length = (
          cameras.focal_length
          + torch.randn_like(cameras.focal_length) * 0.2
      )
  
      # the list of differentiable camera vars
      cam_vars = ("R", "T", "focal_length", "principal_point")
      # enable the gradient caching for the camera variables
      for cam_var in cam_vars:
          getattr(cameras_randomized, cam_var).requires_grad = True
  
      # get the implicit renderer
      images_opacities = ImplicitRenderer(
          raysampler=raysampler, raymarcher=raymarcher
      )(
          cameras=cameras_randomized,
          volumetric_function=spherical_volumetric_function,
          sphere_centroid=sphere_centroid,
          sphere_diameter=sphere_diameter,
      )[
          0
      ]
  
      # check that the renderer does not erase gradients
      loss = images_opacities.sum()
      loss.backward()
      for check_var in (
          *[
              getattr(cameras_randomized, cam_var)
              for cam_var in cam_vars
          ],
          sphere_centroid,
      ):
          self.assertIsNotNone(check_var.grad)
  
      # instantiate the corresponding spherical mesh
      ico = ico_sphere(level=4, device=cameras.device).extend(
          batch_size
      )
      verts = (
          torch.nn.functional.normalize(ico.verts_padded(), dim=-1)
          * sphere_diameter
          + sphere_centroid[:, None]
      )
      meshes = Meshes(
          verts=verts,
          faces=ico.faces_padded(),
          textures=TexturesVertex(
              verts_features=(
                  torch.nn.functional.normalize(verts, dim=-1) * 0.5
                  + 0.5
              )
          ),
      )
  
      # instantiate the corresponding mesh renderer
      lights = PointLights(
          device=cameras.device, location=[[0.0, 0.0, 0.0]]
      )
      renderer_textured = MeshRenderer(
          rasterizer=MeshRasterizer(
              cameras=cameras_randomized,
              raster_settings=RasterizationSettings(
                  image_size=image_size,
                  blur_radius=1e-3,
                  faces_per_pixel=10,
                  z_clip_value=None,
                  perspective_correct=False,
              ),
          ),
          shader=SoftPhongShader(
              device=cameras.device,
              cameras=cameras_randomized,
              lights=lights,
              materials=Materials(
                  ambient_color=((2.0, 2.0, 2.0),),
                  diffuse_color=((0.0, 0.0, 0.0),),
                  specular_color=((0.0, 0.0, 0.0),),
                  shininess=64,
                  device=cameras.device,
              ),
              blend_params=BlendParams(
                  sigma=1e-3,
                  gamma=1e-4,
                  background_color=(0.0, 0.0, 0.0),
              ),
          ),
      )
  
      # get the mesh render
      images_opacities_meshes = renderer_textured(
          meshes, cameras=cameras_randomized, lights=lights
      )
  
      if DEBUG:
          outdir = (
              tempfile.gettempdir() + "/test_implicit_vs_mesh_renderer"
          )
          os.makedirs(outdir, exist_ok=True)
  
          frames = []
          for image_opacity, image_opacity_mesh in zip(
              images_opacities, images_opacities_meshes
          ):
              image, opacity = image_opacity.split([3, 1], dim=-1)
              image_mesh, opacity_mesh = image_opacity_mesh.split(
                  [3, 1], dim=-1
              )
              diff_image = (
                  ((image - image_mesh) * 0.5 + 0.5)
                  .mean(dim=2, keepdim=True)
                  .repeat(1, 1, 3)
              )
              image_pil = Image.fromarray(
                  (
                      torch.cat(
                          (
                              image,
                              image_mesh,
                              diff_image,
                              opacity.repeat(1, 1, 3),
                              opacity_mesh.repeat(1, 1, 3),
                          ),
                          dim=1,
                      )
                      .detach()
                      .cpu()
                      .numpy()
                      * 255.0
                  ).astype(np.uint8)
              )
              frames.append(image_pil)
  
          # export gif
          outfile = os.path.join(outdir, "implicit_vs_mesh_render.gif")
          frames[0].save(
              outfile,
              save_all=True,
              append_images=frames[1:],
              duration=batch_size // 15,
              loop=0,
          )
          print(f"exported {outfile}")
  
          # export concatenated frames
          outfile_cat = os.path.join(
              outdir, "implicit_vs_mesh_render.png"
          )
          Image.fromarray(
              np.concatenate([np.array(f) for f in frames], axis=0)
          ).save(outfile_cat)
          print(f"exported {outfile_cat}")
  
      # compare the renders
      diff = (
          (images_opacities - images_opacities_meshes)
          .abs()
          .mean(dim=-1)
      )
      mu_diff = diff.mean(dim=(1, 2))
      std_diff = diff.std(dim=(1, 2))
      self.assertClose(mu_diff, torch.zeros_like(mu_diff), atol=5e-2)
      self.assertClose(std_diff, torch.zeros_like(std_diff), atol=6e-2)
  
file_path: pytorch3d/tests/test_render_implicit.py
imports: import unittest
  import numpy
  import torch
  from pytorch3d.renderer import BlendParams, EmissionAbsorptionRaymarcher, ImplicitRenderer, Materials, MeshRasterizer, MeshRenderer, MonteCarloRaysampler, MultinomialRaysampler, NDCMultinomialRaysampler, PointLights, RasterizationSettings, ray_bundle_to_ray_points, RayBundle, SoftPhongShader, TexturesVertex
  from pytorch3d.structures import Meshes
  from pytorch3d.utils import ico_sphere
  from .common_testing import TestCaseMixin
  from .test_render_volumes import init_cameras
label_desc: Use image node_id 5 for calling the TestRenderImplicit obj's underlying member method code with example usage: obj._compare_with_meshes_renderer(image_size, batch_size, sphere_diameter) without return types